Jiachen Liu, Rui Yu, Sharon Huang, Hengkai Guo, Sili Chen
We lifted 9 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jcliu0428/ZeroPlane | canonical | 4 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| conv3x3 | Ran | jcliu0428/ZeroPlane/ZeroPlane/modeling/backbone/hrnet.py code served (permissive licence) · get_code("fac5364e2f53c6db") |
| l1_loss | Ran | jcliu0428/ZeroPlane/ZeroPlane/modeling/criterion.py code served (permissive licence) · get_code("2cde99938ddc050b") |
| window_partition | Ran | jcliu0428/ZeroPlane/ZeroPlane/modeling/backbone/swin.py code served (permissive licence) · get_code("f9fd6241d935f07b") |
| window_reverse | Ran | jcliu0428/ZeroPlane/ZeroPlane/modeling/backbone/swin.py code served (permissive licence) · get_code("fb32094c6dbece71") |
| batch_dice_loss | Not yet run | jcliu0428/ZeroPlane/ZeroPlane/modeling/matcher.py code served (permissive licence) · get_code("bc2cb481a75c370d") |
| batch_sigmoid_ce_loss | Not yet run | jcliu0428/ZeroPlane/ZeroPlane/modeling/matcher.py code served (permissive licence) · get_code("1edd24985036b0bf") |
| build_hrnet | Not yet run | jcliu0428/ZeroPlane/ZeroPlane/modeling/backbone/hrnet.py code served (permissive licence) · get_code("0b5d28998e36d4d7") |
| dice_loss | Not yet run | jcliu0428/ZeroPlane/ZeroPlane/modeling/criterion.py code served (permissive licence) · get_code("89f75e54ff128be0") |
| sigmoid_ce_loss | Not yet run | jcliu0428/ZeroPlane/ZeroPlane/modeling/criterion.py code served (permissive licence) · get_code("d0c61e8dba511aa3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
3D plane reconstruction from a single image is a crucial yet challenging topic in 3D computer vision. Previous stateof-the-art (SOTA) methods have focused on training their system on a single dataset from either indoor or outdoor domain, limiting their generalizability across diverse testing data. In this work, we introduce a novel framework dubbed ZeroPlane, a Transformer-based model targeting zero-shot 3D plane detection and reconstruction from a single image, over diverse domains and environments. To enable datadriven models across multiple domains, we have curated a large-scale planar benchmark, comprising over 14 datasets and 560,000 high-resolution, dense planar annotations for diverse indoor and outdoor scenes. To address the challenge of achieving desirable planar geometry on multi-dataset training, we propose to disentangle the representation of plane normal and offset, and employ an exemplar-guided, classification-then-regression paradigm to learn plane and offset respectively. Additionally, we employ advanced backbones as image encoder, and present an effective pixelgeometry-enhanced plane embedding module to further facilitate planar reconstruction. Extensive experiments across multiple zero-shot evaluation datasets have demonstrated that our approach significantly outperforms previous methods on both reconstruction accuracy and generalizability, especially over in-the-wild data. Our code and data are available at: https://github.com/jcliu0428/ZeroPlane.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2506.02493")
get_code_for_paper("2506.02493")
have("2506.02493")
Connect an agent — have() is free.